AI that ships inside your product and measurably moves a number — not a strategy deck.
What this pack is
LLM features, retrieval over your company's own data, chatbots and copilots, and ML features integrated into the product you already run — with evaluation, guardrails and cost control treated as part of the engineering, not an afterthought.
Every business is being told to 'add AI'. Very few are told which feature would actually move a number they care about. That's where we start: discovery identifies the one or two places where a model genuinely earns its keep — answers drawn from your own documents and data, a copilot inside the tool your team already uses, a classifier that removes a manual queue, a WhatsApp assistant that resolves the 60% of queries that repeat. Then we build it properly: retrieval pipelines over your real data, prompts and models chosen by measured evaluation rather than fashion, guardrails for the failure modes that matter in your domain, token costs engineered down before launch, and the whole thing integrated into your existing product with monitoring you can read. No vague AI transformation engagements — a working feature, measured against the baseline, in weeks.
Who this pack is for — and who it isn't
A good fit if
- Companies with a live product and a specific workflow AI could shorten
- Teams sitting on documents, tickets or data no one can query
- Operations with high-volume repetitive queries (support, status, lookups)
- Products that need search, summarisation or extraction that actually works
Probably not for you if
- Businesses with no product yet — build the platform first, then the engine
- Teams that want a demo for a board meeting rather than a feature for users
- Use-cases where a mistake is catastrophic and no human review is possible
Quick facts
- Timeline
- 4–8 weeks from kickoff
- Engagement
- Milestone-based
- Scoping
- Discovery-led, around your process
- After delivery
- Ongoing support available
- Built for
- Growing Indian businesses
- Delivered by
- Aryavrut, est. 2025
Your 4–8 weeks, mapped out
Exactly what happens, week by week. No black box.
- 1Week 1·
Discovery
Workflow and data audit, pick the feature that moves a number, define the measurement baseline
- 2Weeks 2–3·
Prototype & evaluate
Retrieval over your data, model and prompt selection by measured evaluation, first working version
- 3Weeks 4–5·
Integrate & harden
Production integration into your product, guardrails, fallbacks, cost engineering, monitoring
- 4Final week·
Measure & handover
A/B or before/after measurement, documentation, eval harness handover, next-feature shortlist
What's included (week-by-week)
- 01Discovery: the feature chosen for measurable impact, not fashion
- 02Retrieval (RAG) over your own documents and data
- 03Model and prompt selection by evaluation, not default
- 04Guardrails, fallbacks and human-review paths
- 05Integration into your existing product and auth
- 06Cost engineering and usage monitoring
- 07Eval harness and documentation handed over
What you provide
- Access to the data or documents the feature draws on
- A domain expert for a few hours a week during evaluation
- A decision-maker for the launch threshold
What you walk away with at week 6
Concrete artifacts you can point to. No vague "transformation" — actual stuff that runs in production.
- One AI feature live in your product, measured against the pre-launch baseline
- An evaluation harness you can rerun when models or prompts change
- Guardrails and fallbacks for the failure modes we identified together
- Token/API cost model engineered and documented before scale-up
Why teams pick this pack over the alternatives
Honest comparison. We name the alternatives and tell you when they're the better choice.
vs. AI strategy consultancy
Why pick AI strategy consultancy
Big-picture roadmaps, executive alignment
Why pick this pack
Working software in production, measured — strategy emerges from what demonstrably works.
vs. In-house experimentation
Why pick In-house experimentation
Builds internal capability over time
Why pick this pack
A team that has already made the model, retrieval and cost mistakes — on someone else's budget.
vs. Off-the-shelf AI add-ons
Why pick Off-the-shelf AI add-ons
Switched on in a day
Why pick this pack
Grounded in your data and workflows, not generic answers bolted onto the UI.
AI Engine — questions we get a lot
How is a AI Engine engagement structured?
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It's a defined scope delivered in 4–8 weeks, scoped around your process in a discovery call. The engagement is milestone-based, and the full scope is confirmed in your proposal. Request a proposal to get the specifics for your situation.
How long does AI Engine take?
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4–8 weeks from project kickoff. Timelines are calendar weeks and we share the week-by-week breakdown on this page.
What's included in AI Engine?
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Discovery: the feature chosen for measurable impact, not fashion; Retrieval (RAG) over your own documents and data; Model and prompt selection by evaluation, not default; Guardrails, fallbacks and human-review paths; Integration into your existing product and auth; Cost engineering and usage monitoring; Eval harness and documentation handed over
What happens after AI Engine is delivered?
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You own the full source, infrastructure, and documentation. Ongoing support — bug fixes, security patches, and minor enhancements — is available after launch, with terms confirmed in your proposal.
What does the customer need to provide?
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Access to the data or documents the feature draws on; A domain expert for a few hours a week during evaluation; A decision-maker for the launch threshold
Tech stack
Production-grade, modern, well-documented stack. No exotic frameworks no one else can maintain. You can hire any competent engineer to extend this code.
Common add-ons
Most clients pick 0–2 of these. We'll suggest only what you actually need on the discovery call, and scope them into your proposal.
Document AI pipeline
OCR and extraction at volume — see the Document Processing line
WhatsApp / voice assistant channel
The same engine, reachable where your customers already are
Model & cost review retainer
Quarterly re-evaluation as models and prices move
Looking for something else?
Web Development
The platforms that run your business — CRM, HRMS, ERP, portals and the site that represents you.
App Development
iOS and Android apps taken from spec to the app stores — and kept alive after.
Document Processing
Paper in, structured data out — invoices, contracts, site records and KYC, without the typing.
AI Engine — from our blog
Integrating AI into Business Applications: A Practical Guide for Indian SMBs (2026)
How Indian businesses can add real, measurable AI features to their software — chatbots, document processing, predictive analytics, personalization — without a vague, expensive 'AI strategy'.
Implementing Omnichannel Customer Service in India (2026)
How Indian businesses can deliver seamless service across WhatsApp, phone, email, and social — a unified customer view, channel integration, and the CRM foundation that makes it work.
Progressive Web Apps (PWA): A Complete Guide for 2026
What PWAs are, why they suit the Indian market's data and storage constraints, and how to decide between a PWA and a native app for your product.
Start AI Engine.
AI that ships inside your product and measurably moves a number — not a strategy deck. Delivered in 4–8 weeks, scoped around your process. Send a few details and we'll confirm whether this pack fits — we reply within 1 business day.
Request a proposal — AI Engine
Tell us what you need — we reply within 1 business day.
Ready to start?
We reply within 1 business day. We'll either confirm the pack fits, or tell you honestly that it doesn't.